@inproceedings{scholars4258,
            year = {2014},
             doi = {10.1109/ICCOINS.2014.6868359},
            note = {cited By 6; Conference of 2014 International Conference on Computer and Information Sciences, ICCOINS 2014 ; Conference Date: 3 June 2014 Through 5 June 2014; Conference Code:112912},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {2014 International Conference on Computer and Information Sciences, ICCOINS 2014 - A Conference of World Engineering, Science and Technology Congress, ESTCON 2014 - Proceedings},
           title = {Parallel Kalman filter-based multi-human tracking in surveillance video},
          author = {Yussiff, A.-L. and Yong, S.-P. and Baharudin, B. B.},
            isbn = {9781479943913},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938777988&doi=10.1109\%2fICCOINS.2014.6868359&partnerID=40&md5=5c6738c8ed0d3d423c2955feac8198be},
        keywords = {Computer graphics; Computer graphics equipment; Graphics processing unit; Program processors; Security systems; Target tracking, Human Tracking; Kalman filtering algorithms; Multi-human tracking; Multi-person tracking; Multiple target tracking; Parallel implementations; Parallel-computing environment; Standard Kalman filters, Kalman filters},
        abstract = {A novel approach to robust and flexible person tracking using an algorithm that integrates state of the arts techniques; an Enhanced Person Detector (EPD) and Kalman filtering algorithm. This proposed algorithm employs multiple instances of Kalman Filter with complex assignment constraints using Graphics Processing Unit (GPU-NVDIA CUDA) as a parallel computing environment for tracking multiple persons even in the presence of occlusion. A Kalman filter is a recursive algorithm which predict the state variables and further uses the observed data to correct the predicted value. Data association in different frames are solved using Hungarian technique to link data in previous frame to the current frame. The benefit of this research is an adoption of standard Kalman Filter for multiple target tracking of humans in real time. This can further be used in all applications where human tracking is needed. The parallel implementation has increased the frame processing speed by 20-30 percent over the CPU implementation. {\^A}{\copyright} 2014 IEEE.}
}